Account Management

Teams & Rankings

Teams

TeamWinsLossesCompetition LevelGender DivisionCityState
BDC 48ClubMenWashington DC
Brookwood High School (Inferno) 35High SchoolBoysSnellvilleGA
Carleton College-C (Nova) 14CollegeWomenNorthfieldMN
Denver Boulder Summer Youth Ultimate League 2022 00LeagueMixedDenverCO
'Shine 24ClubMixedNashvilleTN
(washed) 64ClubMenDenverCO
167529 38906 116336 233005 172648 221856 161337 229298 223496 309674 271239 234658 263026 204588 182 00LeagueMixedTrentonNJ
2024 West WUC MMP Tryouts 00US National TeamMenColorado SpringsCO
2024 West WUC WMP Tryouts 00US National TeamWomenColorado SpringsCO
2024 Adult Spring League session 1 00LeagueMixedPortlandME
2024 Albany Mixed Winter League 00LeagueMixedAlbanyNY
2024 AUDA Spring League 00LeagueMixedAlbanyNY
2024 AUDA Wom&ns Indoor League 00LeagueWomenAlbanyNY
2024 Beach Indoor 00LeagueMixedNew YorkNY
2024 Charlottesville Spring LTP 00Youth ClinicMixedCharlottesvilleVA
2024 MUD Spring League 00LeagueMixedNew YorkNY
2024 NC Middle School Championships 00Middle SchoolMixedRaleighNC
2024 Spring Mixed Goalti League 00LeagueMixedDurhamNC
2024 Survival* Winter Pickup 00LeagueWomenNew YorkNY
2024 Team USA Men 00ClubMenColorado SpringsCO
Rows: 1 - 20 of 2672

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NameStatusRank SetCreate DateLinks
2024 College Women's Div — end of season rankings Published College Women 6/20/2024 11:04:42 AM Team Rankings
2024 College Men's Div — end of season rankings Published College Men 6/20/2024 10:36:28 AM Team Rankings
2023 TCT Men's Final Rankings Published Club Men 10/28/2023 9:19:38 PM Team Rankings
2023 TCT Mixed Final Rankings Published Club Mixed 10/28/2023 9:19:05 PM Team Rankings
2023 TCT Women's Final Rankings Published Club Women 10/28/2023 9:18:32 PM Team Rankings
Rows: 1 - 5 of 5   Page: 1 of 1
 
About the USA Ultimate Rankings Algorithm
 
The USA Ultimate Rankings (version 2.0) are used in the College and Club regular seasons to help determine strength wildcard bid allocation throughout the series. The algorithm becomes more accurate later in the season as more valid scores are provided by tournament directors and teams.
 
The most basic explanation of this rating system is this: for each game a team plays, the team gets a game rating. These ratings are then averaged over all games played by a team.
 
The next level of complexity is how to compute the points for a given game, and how to average them.  The points for a given game is given by the opponent's rating plus or minus (in the case of a win or loss, respectively) some rating differential x.
 
The number x is a function of the score. Here is the general formula for it:
 
Algo
 Algo2
 
The function was chosen to have the following properties:
  • each additional goal is worth more when games are close than when they are not;
  • every game decided by one point gets the same differential of 125, no matter the game total;
  • the maximum x can be is 600;
  • a game earns the maximum differential if and only if the winning score is more than twice the losing score.
Here is a partial table of differentials under this formula:
 
Algo3
Algo3
 
As an example, suppose team A beats team B 15-11. For a 15-11 game, x = 381. Suppose, further, that team B has a rating of 1010. Then the game rating earned by team A is 1010 381, or 1391.
 
So, suppose team A has played in 4 games, and each individual game rating is 1298, 913, 1410, and 1103. To determine A's overall rating, we take a weighted average of those numbers. The weight of any game is a product of the date weight and the score weight. The date weight depends upon how recently the game was played; games in the first week of the regular season will get a date weight of 0.5, while games in the last week of the season get a date weight of 1.0. Date weights of games in intermediate weeks are interpolated exponentially, so each week's date weight is a fixed multiple of the previous week's date weight. The score weight of a game will be 1.0 if the winning score is at least 13, or if the total score is at least 19. Games with a winning score below 13 and a total score below 19 will get lower score weights. Precisely, the score weight, where W and L denote the winning and losing scores, will be:
AlgorithmUpdate_ScoreWeight_2018
 
AlgorithmUpdate1.2018
 
This averaging is done in an iterative process. First, we temporarily set every team's rating to 1000, and compute each individual game rating and take averages. This gives each team a new rating. Then, we re-compute the game ratings and averages, using this new set of ratings, to get yet another set of ratings.  We do this thousands of times until the rating for each team has stabilized. If some team does really well, and, after the first iteration their rating goes up 250 points, then, on the second iteration, all teams that have played the first team will go up by a smaller amount, and on the third iteration, all the teams that have played the teams that played the first team will go up by a small amount, and so on.
 
Finally, if a team is rated more than 600 points higher than its opponent, and wins with a score that is more than twice the losing score plus one, the game is ignored for ratings purposes. However, this is only done if the winning team has at least N other results that are not being ignored, where N=5.
 
USA Ultimate continues to look to improve its rankings algorithm through consultation with volunteers on the Algorithm Taskforce.  The review process is ongoing as we look to enhance the level of competition and goals of USA Ultimate. Any additional changes/alterations to the Algorithm will be communicated as they occur.
 
Below is a description of a change to the rankings algorithm used in 2013:
 
The modification to the algorithm removes the possibility that a team rated more than 600 points higher than its opponent will drop in rating when beating that team by a large enough point differential. The game is preserved to serve as a connection between teams but does not impact the actual rating of either of the respective teams involved. USA Ultimate continues to look to improve its rankings algorithm through consultation with Rodney Jacobson, Sholom Simon and other volunteers on the Algorithm Taskforce.

 
  College Rankings: 1987-2015   Club Rankings: 2012-2015
 
 
  2010s   2000s   1990s   1980s  
      2009   1999   1989  
      2008   1998      
      2007   1997   1987  
      2006   1996      
  2015   2005   1995      
  2014   2004   1994      
  2013   2003   1993      
  2012   2002   1992      
  2011   2001   1991      
  2010   2000   1990      
 
 
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